---
title: 'SugarcaneLD-BD: Bangladeshi Leaf-Disease Dataset'
url: https://www.emergentmind.com/topics/sugarcaneld-bd
type: topic
---

# SugarcaneLD-BD: Bangladeshi Leaf-Disease Dataset

SugarcaneLD-BD is a curated dataset for sugarcane leaf-disease classification introduced in the paper “SugarcaneShuffleNet: A Very Fast, Lightweight Convolutional Neural Network for Diagnosis of 15 Sugarcane Leaf Diseases.” It contains **638 curated images across five classes**, including **four major sugarcane diseases** and **one healthy class**, collected **in Bangladesh under diverse field conditions and verified by expert pathologists**. In the paper’s contribution structure, SugarcaneLD-BD is the dataset component, while **SugarcaneShuffleNet** is the model contribution and **SugarcaneAI** is the deployment component [2508.17107].

## 1. Dataset identity and rationale

SugarcaneLD-BD was introduced to address the scarcity of public, field-representative sugarcane disease data. The paper states that existing general plant datasets such as **PlantVillage** and **PlantDoc** do not include sugarcane leaf diseases, and that existing sugarcane datasets are limited by geography, class coverage, or labeling consistency. The dataset is positioned as a Bangladesh-specific contribution intended to provide **regional realism**, **expert-verified labels**, and **field variability** in background, viewpoint, and lighting [2508.17107].

The authors explicitly argue that disease appearance depends on local agroecological conditions. On that basis, SugarcaneLD-BD is presented not merely as an additional image collection, but as a dataset meant to capture Bangladesh-relevant phenotypes that may not be represented well by Indian datasets alone. The paper further states that SugarcaneLD-BD can be merged with existing datasets to produce a larger and more representative corpus, which is exactly how it is used in the reported experiments [2508.17107].

A common misunderstanding is to treat SugarcaneLD-BD as a fully benchmarked standalone dataset with its own train/validation/test protocol and headline metrics. The paper does not do that. Instead, it introduces SugarcaneLD-BD as the local-data foundation of a broader benchmarking and deployment pipeline, and all reported model results are on a merged multi-source corpus rather than on SugarcaneLD-BD in isolation [2508.17107].

## 2. Acquisition, geography, and annotation

SugarcaneLD-BD was collected from **four sugarcane-producing locations in Bangladesh**, selected to reflect different agroecological conditions, farming practices, and disease prevalence [2508.17107].

| Location | Coordinates | Agroecological zone |
|---|---|---|
| BSRI research field, Ishwardi, Pabna | 24.130000° N, 89.060000° E | Active Ganges Floodplain (AEZ 10) |
| BSRI Regional Station, Gazipur | 23.999941° N, 90.420273° E | Middle Meghna River Floodplain (AEZ 16) |
| Farmer fields in Narsingdi | 23.920700° N, 90.718800° E | Old Meghna Estuarine Floodplain (AEZ 15) |
| Farmer fields in Natore | 24.411000° N, 89.012000° E | High Ganges River Floodplain (AEZ 11) |

The acquisition period was **September and October 2023**. Two smartphones were used: **Realme 8** and **Xiaomi Redmi Note 11**. The paper reports the following native image specifications: **Realme 8: 2608 × 4624, RGB, focal length 4.71 mm, aperture f/1.8, exposure 1/104 s, ISO 100**; **Xiaomi Redmi Note 11: 2296 × 4080, RGB, focal length 4.25 mm, aperture f/1.8, exposure 1/120 s, ISO 58**. Images were captured under **varying lighting conditions**, at **different times of day**, from **multiple angles**, and with **diverse backgrounds**, emphasizing field realism rather than controlled capture [2508.17107].

All images were then **resized to 224 × 224 pixels**. The stated rationale is standard CNN input compatibility, simplification of the input pipeline, and reduced computational complexity. No other preprocessing steps are reported for SugarcaneLD-BD itself beyond resizing [2508.17107].

The annotation regime is described as expert-led: images were **carefully labeled by qualified plant pathologists**, the dataset was **expert-annotated**, and labels were **verified by expert pathologists**. At the same time, the paper does not specify the number of experts, whether annotation was independent or consensus-based, whether disagreements were adjudicated, or whether laboratory confirmation was used. This leaves the validation protocol identifiable in principle but under-specified in procedure [2508.17107].

## 3. Class taxonomy and dataset composition

SugarcaneLD-BD contains **five classes total**: **four disease classes** and **one healthy class**. The four disease classes described in detail are **Red Rot**, **Ring Spot**, **Red Leaf Spot**, and **Eye Spot** [2508.17107].

| Class | Description in the paper | Count explicitly reported for SugarcaneLD-BD |
|---|---|---:|
| Red Rot | Caused by *Colletotrichum falcatum*; described as the most damaging sugarcane disease in Bangladesh | Not isolated |
| Ring Spot | Caused by *Leptosphaeria sacchari*; lesions with reddish-brown borders | 83 |
| Red Leaf Spot | Caused by *Dimeriella sacchari*; small red spots merging into larger lesions | 43 |
| Eye Spot | Caused by *Bipolaris sacchari*; long lesions with red centers and a narrow yellow chlorotic ring | 75 |
| Healthy | Healthy class mentioned in the composition table | Not isolated |

The dataset total of **638 images** is stated multiple times. However, the paper only explicitly isolates per-class counts for **Eye Spot (75)**, **Red Leaf Spot (43)**, and **Ring Spot (83)**. It does **not** explicitly isolate SugarcaneLD-BD-specific counts for **Red Rot** and **Healthy**. The paper notes that the implied combined remainder for **Healthy + SugarcaneLD-BD Red Rot** is **437 images**, but does not break that remainder down [2508.17107].

This partial transparency in class counts is important for interpretation. SugarcaneLD-BD is clearly defined taxonomically, but its fully enumerated per-class distribution is not reported in the same level of detail for all classes. A second point of clarification is that the paper does **not** report a standalone train/validation/test split for SugarcaneLD-BD, does **not** report a standalone augmentation protocol for it, and does **not** report standalone benchmark results on it [2508.17107].

## 4. Incorporation into the merged 17-class benchmark

Experimentally, SugarcaneLD-BD is merged with two Indian sugarcane disease datasets: the **Thite et al.** dataset from India with **11 classes** and **6748 images**, and the **Daphal and Koli** dataset from India with **5 classes** and **2521 images**. The paper states that the initial merged corpus had **17 distinct classes** and **9,908 images total**. It also notes a minor arithmetic inconsistency, because **6748 + 2521 + 638 = 9907**, not 9908, although the official figure given repeatedly in the source text is **9,908** [2508.17107].

The merged class list includes **Banded Chlorosis, Brown Rust, Brown Spot, Dried Leaves, Eye Spot, Grassy Shoot, Healthy, Mosaic, Pokkah Boeng, Red Rot / RedRot, Red Leaf Spot, Ring Spot, Rust, Sett Rot, Smut, Viral Disease,** and **Yellow Leaf**. The paper indicates conceptual class harmonization across sources, but does not provide a canonical source-to-target mapping table. It also leaves some naming ambiguity, particularly the coexistence of **Brown Rust** and **Rust**, and the alternating use of **Red Rot** and **RedRot** [2508.17107].

A substantial curation step follows the merge. The paper reports that **exact duplicates** were removed using **MD5 hashing**, and **near-duplicates** were removed using a **5-bit difference threshold**. The results are reported as follows: **1,264 exact duplicates removed**, **1,607 near-duplicates removed**, and a **final deduplicated dataset of 7,037 images**. Files were renamed into a standardized format such as `ClassName_0001.jpg` [2508.17107].

The final deduplicated corpus remains strongly imbalanced. The paper reports that the imbalance ratio improved from **26:1** to **3.8:1** after training-set augmentation. For the combined dataset, the split is **80% train** and **20% test** with **stratified sampling**, yielding **5623** pre-augmentation training images and **1414** test images. Augmentation is applied **only to the training set**, with class-dependent multiplicities; for example, **Eye Spot**, **Red Leaf Spot**, and **Ring Spot** each receive six augmentations per training image, while larger classes such as **Brown Spot**, **Healthy**, **RedRot**, and **Yellow Leaf** receive none. The final augmented training set contains **11,313 images**, including **5,691 additional generated training images** [2508.17107].

## 5. Benchmarking, SugarcaneShuffleNet, and deployment ecology

The paper’s central benchmark results are not reported for SugarcaneLD-BD alone, but for the **combined 17-class deduplicated dataset that includes SugarcaneLD-BD**. Within that setting, **SugarcaneShuffleNet** is presented as an optimized ShuffleNet-based lightweight CNN chosen for edge deployment because of its favorable balance between accuracy and efficiency. Its best hyperparameters were identified with **Optuna** using the **Tree-structured Parzen Estimator**, with **20 trials** and **25 epochs per trial**, and full training used **CrossEntropyLoss**, **batch size 32**, **CosineAnnealingLR**, **100 epochs**, and **early stopping with patience 10** [2508.17107].

On the merged benchmark, **SugarcaneShuffleNet** achieves **98.02% accuracy**, **0.98 precision**, **0.98 recall**, and **0.98 F1**, with **2.19 M parameters**, **152.43 MMACs**, **9.26 MB** model size, and **4.14 ms** average inference time per image. The same table shows that **MnasNet** reaches **98.51%** accuracy, but the paper selects SugarcaneShuffleNet as the preferred deployment trade-off because MnasNet requires more parameters, memory, and computation [2508.17107].

The per-class F1 scores are particularly informative for the classes originating from SugarcaneLD-BD. On the combined benchmark, SugarcaneShuffleNet scores **0.97** for **Eye Spot**, **0.89** for **Red Leaf Spot**, **1.00** for **Ring Spot**, **0.98** for **Red Rot**, and **0.99** for **Healthy**. This shows that the hardest SugarcaneLD-BD-derived class in the merged benchmark is **Red Leaf Spot**, while **Ring Spot** and **Healthy** are nearly saturated under the reported evaluation protocol [2508.17107].

The same paper integrates the model into **SugarcaneAI**, a **Progressive Web Application**. The application accepts uploaded or real-time images, returns the **top predicted class**, **confidence score**, **top five predicted classes**, and a **Grad-CAM heatmap**, and can request recommendations from the **Gemini API**. The authors state that Grad-CAM heatmaps focus on meaningful disease features such as **chlorotic bands**, **necrotic rings**, **rust pustules**, and symptomatic **red-rot** regions. These deployment details matter because SugarcaneLD-BD is not treated in isolation; it functions as the data substrate for a full data-model-application stack [2508.17107].

## 6. Position within the sugarcane disease-data landscape

SugarcaneLD-BD occupies a specific niche within sugarcane disease datasets: it is a **Bangladesh-origin, expert-annotated, field-acquired RGB leaf dataset** intended for disease classification under variable real-world capture conditions. It is neither the first public sugarcane disease image dataset nor a universal sugarcane health benchmark. Earlier work such as “SugarcaneNet” uses a different public benchmark, the **Sugarcane Leaf Disease Dataset** from **Mendeley Data**, with **2569 images** from **Maharashtra, India**, split into **Healthy, Mosaic, RedRot, Rust,** and **Yellow disease**, and that paper explicitly does **not** call its dataset “SugarcaneLD-BD” [2403.18870].

SugarcaneLD-BD is also distinct from remote-sensing disease datasets and from hyperspectral screening studies. The broader sugarcane monitoring literature includes satellite-based multispectral detection of asymptomatic **Ratoon Stunting Disease**, where **SVM-RBF** achieved classification accuracy between **85.64% and 96.55%** depending on variety using **Sentinel-2**-derived vegetation indices [2410.03141]. A review of sugarcane health monitoring further emphasizes that satellite disease literature remains sparse and confounded by **variety**, **crop age**, **soil background**, **viewing angle**, **weather**, and **atmospheric effects**, even though the approach is considered promising [2404.16844]. In that context, SugarcaneLD-BD represents a leaf-level RGB benchmark rather than a canopy-scale or satellite-scale disease-monitoring system.

Its principal strengths, as stated in the paper, are **regional novelty**, **field realism**, **multi-zone agroecological diversity**, **expert validation**, and **public availability on Mendeley Data and Kaggle**. Its principal limitations are equally explicit: **small size** at **638 images**, severe imbalance for rare classes, incomplete annotation-protocol detail, no standalone split or standalone benchmark results, and no formal cross-region, cross-device, or cross-lighting generalization analysis [2508.17107].

A reasonable synthesis is that SugarcaneLD-BD is best understood as a curated, Bangladesh-specific leaf-disease dataset whose importance lies less in standalone scale than in the way it injects new geography, new phenotypes, and field-acquired variability into a merged 17-class sugarcane disease benchmark. In the source paper, its scientific role is foundational rather than self-sufficient: it fills a public data gap, sharpens regional relevance, and supports the training and deployment of lightweight sugarcane disease classifiers under low-resource conditions [2508.17107].

Source: https://www.emergentmind.com/topics/sugarcaneld-bd